System and method to improve multi-camera monocular depth estimation using pose averaging

ABSTRACT

A method for multi-camera monocular depth estimation using pose averaging is described. The method includes determining a multi-camera photometric loss associated with a multi-camera rig of an ego vehicle. The method also includes determining a multi-camera pose consistency constraint (PCC) loss associated with the multi-camera rig of the ego vehicle. The method further includes adjusting the multi-camera photometric loss according to the multi-camera PCC loss to form a multi-camera PCC photometric loss. The method also includes training a multi-camera depth estimation model and an ego-motion estimation model according to the multi-camera PCC photometric loss. The method further includes predicting a 360° point cloud of a scene surrounding the ego vehicle according to the trained multi-camera depth estimation model and the ego-motion estimation model.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No. 17/377,684, filed Jul. 16, 2021, and titled “SYSTEM AND METHOD TO IMPROVE MULTI-CAMERA MONOCULAR DEPTH ESTIMATION USING POSE AVERAGING,” and claims the benefit of U.S. Provisional Patent Application No. 63/161,614, filed Mar. 16, 2021, and titled “SCALE-AWARE DEPTH ESTIMATION USING MULTI-CAMERA PHOTOMETRIC LOSS,” the disclosures of which are expressly incorporated by reference herein in their entireties.

BACKGROUND Field

Certain aspects of the present disclosure generally relate to machine learning and, more particularly, a system and method to improve multi-camera monocular depth estimation using pose averaging.

Background

Autonomous agents rely on machine vision for sensing a surrounding environment by analyzing areas of interest in images of the surrounding environment. Although scientists have spent decades studying the human visual system, a solution for realizing equivalent machine vision remains elusive. Realizing equivalent machine vision is a goal for enabling truly autonomous agents. Machine vision is distinct from the field of digital image processing because of the desire to recover a three-dimensional (3D) structure of the world from images and using the 3D structure for fully understanding a scene. That is, machine vision strives to provide a high-level understanding of a surrounding environment, as performed by the human visual system.

Autonomous agents may rely on a trained convolutional neural network (CNN) to identify objects within areas of interest in an image of a surrounding scene of the autonomous agent. For example, a CNN may be trained to identify and track objects captured by sensors, such as light detection and ranging (LIDAR) sensors, sonar sensors, red-green-blue (RGB) cameras, RGB-depth (RGB-D) cameras, and the like. The sensors may be in communication with a device, such as an autonomous vehicle.

Current self-supervised depth and pose architectures are either monocular or stereo in nature, precluding the ability to estimate camera motion independently from multiple cameras. Monocular networks use a single camera, and pose estimation is performed by learning frame-to-frame motion. By contrast, stereo methods train on images pairs acquired at the same time-step without considering temporal context. Instead, stereo methods use a known baseline between cameras and, therefore, forego pose calculation pose between the images.

SUMMARY

A method for multi-camera monocular depth estimation using pose averaging is described. The method includes determining a multi-camera photometric loss associated with a multi-camera rig of an ego vehicle. The method also includes determining a multi-camera pose consistency constraint (PCC) loss associated with the multi-camera rig of the ego vehicle. The method further includes adjusting the multi-camera photometric loss according to the multi-camera PCC loss to form a multi-camera PCC photometric loss. The method also includes training a multi-camera depth estimation model and an ego-motion estimation model according to the multi-camera PCC photometric loss. The method further includes predicting a 360° point cloud of a scene surrounding the ego vehicle according to the trained multi-camera depth estimation model and the ego-motion estimation model.

A non-transitory computer-readable medium having program code recorded thereon for multi-camera monocular depth estimation using pose averaging is described. The program code is executed by a processor. The non-transitory computer-readable medium includes program code to determine a multi-camera photometric loss associated with a multi-camera rig of an ego vehicle. The non-transitory computer-readable medium also includes program code to determine a multi-camera pose consistency constraint (PCC) loss associated with the multi-camera rig of the ego vehicle. The non-transitory computer-readable medium further includes program code to adjust the multi-camera photometric loss according to the multi-camera PCC loss to form a multi-camera PCC photometric loss. The non-transitory computer-readable medium also includes program code to train a multi-camera depth estimation model and an ego-motion estimation model according to the multi-camera PCC photometric loss. The non-transitory computer-readable medium further includes program code to predict a 360° point cloud of a scene surrounding the ego vehicle according to the trained multi-camera depth estimation model and the ego-motion estimation model.

A system for multi-camera monocular depth estimation using pose averaging is described. The system includes a multi-camera photometric loss module to determine a multi-camera photometric loss associated with a multi-camera rig of an ego vehicle. The system also includes a multi-camera pose consistency constraint (PCC) loss module to determine a multi-camera PCC loss associated with the multi-camera rig of the ego vehicle and to adjust the multi-camera photometric loss according to the multi-camera PCC loss to form a multi-camera PCC photometric loss. The system further includes a depth and ego-motion training module to train a multi-camera depth estimation model and an ego-motion estimation model according to the multi-camera PCC photometric loss. The system also includes a full surround mono-depth (FSM) 360° point cloud prediction module to predict a 360° point cloud of a scene surrounding the ego vehicle according to the trained multi-camera depth estimation model and the ego-motion estimation model.

This has outlined, rather broadly, the features and technical advantages of the present disclosure in order that the detailed description that follows may be better understood. Additional features and advantages of the present disclosure will be described below. It should be appreciated by those skilled in the art that the present disclosure may be readily utilized as a basis for modifying or designing other structures for carrying out the same purposes of the present disclosure. It should also be realized by those skilled in the art that such equivalent constructions do not depart from the teachings of the present disclosure as set forth in the appended claims. The novel features, which are believed to be characteristic of the present disclosure, both as to its organization and method of operation, together with further objects and advantages, will be better understood from the following description when considered in connection with the accompanying figures. It is to be expressly understood, however, that each of the figures is provided for the purpose of illustration and description only and is not intended as a definition of the limits of the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The features, nature, and advantages of the present disclosure will become more apparent from the detailed description set forth below when taken in conjunction with the drawings in which like reference characters identify correspondingly throughout.

FIG. 1 illustrates an example implementation of designing a system using a system-on-a-chip (SOC) for multi-camera monocular depth estimation using pose averaging, in accordance with aspects of the present disclosure.

FIG. 2 is a block diagram illustrating a software architecture that may modularize functions for multi-camera monocular depth estimation using pose averaging, according to aspects of the present disclosure.

FIG. 3 is a diagram illustrating an example of a hardware implementation for a multi-camera monocular depth estimation system, according to aspects of the present disclosure.

FIG. 4 is a drawing illustrating an example of an ego vehicle in an environment, according to aspects of the present disclosure.

FIG. 5 is a diagram illustrating a full surround mono-depth (FSM) point cloud generated using self-supervised learning of scale-aware and consistent depth networks in a wide-baseline 360° multi-camera setting, according to aspects of the present disclosure.

FIG. 6 is a diagram illustrating a distribution of a multi-camera rig of an ego vehicle, with each camera pointing in a different direction to generate a 360° view of an environment surrounding the ego vehicle, according to aspects of the present disclosure.

FIG. 7 is a diagram illustrating multi-camera spatio-temporal transformation matrices to enable warping of images between different cameras taken at different time-steps, according to aspects of the present disclosure.

FIG. 8 is a drawing illustrating image warping for spatial and temporal contexts to generate a multi-camera photometric loss, according to aspects of the present disclosure.

FIG. 9 is a flowchart illustrating a method for multi-camera monocular depth estimation using pose averaging, according to aspects of the present disclosure.

DETAILED DESCRIPTION

The detailed description set forth below, in connection with the appended drawings, is intended as a description of various configurations and is not intended to represent the only configurations in which the concepts described herein may be practiced. The detailed description includes specific details for the purpose of providing a thorough understanding of the various concepts. It will be apparent to those skilled in the art, however, that these concepts may be practiced without these specific details. In some instances, well-known structures and components are shown in block diagram form in order to avoid obscuring such concepts.

Based on the teachings, one skilled in the art should appreciate that the scope of the present disclosure is intended to cover any aspect of the present disclosure, whether implemented independently of or combined with any other aspect of the present disclosure. For example, an apparatus may be implemented or a method may be practiced using any number of the aspects set forth. In addition, the scope of the present disclosure is intended to cover such an apparatus or method practiced using other structure, functionality, or structure and functionality in addition to, or other than the various aspects of the present disclosure set forth. It should be understood that any aspect of the present disclosure disclosed may be embodied by one or more elements of a claim.

Although particular aspects are described herein, many variations and permutations of these aspects fall within the scope of the present disclosure. Although some benefits and advantages of the preferred aspects are mentioned, the scope of the present disclosure is not intended to be limited to particular benefits, uses, or objectives. Rather, aspects of the present disclosure are intended to be broadly applicable to different technologies, system configurations, networks and protocols, some of which are illustrated by way of example in the figures and in the following description of the preferred aspects. The detailed description and drawings are merely illustrative of the present disclosure, rather than limiting the scope of the present disclosure being defined by the appended claims and equivalents thereof.

The ability to reconstruct the structure of a scene with high accuracy is fundamental to ensuring robust autonomous navigation. Traditional approaches to monocular 3D reconstruction rely on hand-engineered features to reliably reconstruct scenes from camera imagery. More recently, deep learning approaches show considerable promise in eliminating these hand-engineered features for further improving 3D reconstruction. In particular, these deep learning approaches are especially helpful for ill-posed regimes, such as reconstructing textureless regions, or geometrically under-determined regimes.

Self-supervised learning enables the extraction of information from arbitrarily large amounts of data. Depth is particularly suitable for this task because depth is trained using geometry constraints, such as structure-from-motion in raw video sequences. Unfortunately, the photometric loss commonly used when training such networks is scale-agnostic. That is, scale-agnostic photometric loss is limited to generating models accurate up to an unknown factor. This prevents self-supervised depth models (albeit highly accurate) from being applicable in a wide range of downstream tasks where a metric scale is specified, such as 3D object detection.

Multi-camera approaches to self-supervised depth and ego-motion are currently restricted to a stereo setting, with rectified images that enable disparity learning, which is converted to depth through a known baseline. Although methods are proposed that combine stereo and monocular self-supervision, directly regressing depth from stereo pairs assumes the availability of highly-overlapping images from available datasets and/or accurate velocity estimates. The availability of extrinsics is an easier specification than accurate velocity estimates or highly overlapping images because extrinsics are obtained off-line with off-the-shelf calibration techniques that achieve high precision. In addition, intrinsic information is readily available by using similar calibration techniques.

A known limitation of self-supervised depth and ego-motion models is their inability to generate metrically accurate models in multi-camera settings. Current self-supervised depth and pose architectures are either monocular or stereo in nature, precluding the ability to estimate camera motion independently from multiple cameras. Monocular networks use a single camera, and pose estimation is performed by learning frame-to-frame motion. By contrast, stereo methods train on images pairs acquired at the same time-step without considering a temporal context. Instead, stereo methods use a known baseline between cameras and, therefore, forego pose calculation pose between the images.

Aspects of the present disclosure are directed to facilitating consistent multi-camera learning of camera pose. Current monocular depth estimation architectures are generally composed of two parts: a depth network (e.g., that predicts a depth map from a single image) and a pose network (e.g., that predicts a camera pose/motion between two successive images). These aspects of the present disclosure involve an architecture that learns consistent pose from multiple cameras attached to an autonomous vehicle (e.g., a multi-camera rig or any other robot rig). In one aspect of the present disclosure, a method takes successive frames from all cameras as input. For example, these camera frames may or may not be overlapping, as long as the extrinsics between the cameras are known). In this example, N independent pose predictions are provided for N cameras. Nevertheless, these poses are essentially estimating the same motion, just in a different coordinate frame.

Some aspects of the present disclosure project predicted transformations into a common coordinate frame. For example, a common coordinate frame may include car centric coordinates with respect to the center of the rear axle. Once the predicted transformation is projected into the common coordinate frame, a penalty is added for any individual camera pose prediction deviating from the average of the pose predictions from each of the cameras. In addition, rotation and translation are handled independently to account for the different scale between the two predictions. This constraint improves both the pose prediction and the depth prediction for each of the cameras of the multi-camera rig.

FIG. 1 illustrates an example implementation of the aforementioned system and method for multi-camera monocular depth estimation based on pose averaging using a system-on-a-chip (SOC) 100 of an ego vehicle 150. The SOC 100 may include a single processor or multi-core processors (e.g., a central processing unit (CPU) 102), in accordance with certain aspects of the present disclosure. Variables (e.g., neural signals and synaptic weights), system parameters associated with a computational device (e.g., neural network with weights), delays, frequency bin information, and task information may be stored in a memory block. The memory block may be associated with a neural processing unit (NPU) 108, a CPU 102, a graphics processing unit (GPU) 104, a digital signal processor (DSP) 106, a dedicated memory block 118, or may be distributed across multiple blocks. Instructions executed at a processor (e.g., CPU 102) may be loaded from a program memory associated with the CPU 102 or may be loaded from the dedicated memory block 118.

The SOC 100 may also include additional processing blocks configured to perform specific functions, such as the GPU 104, the DSP 106, and a connectivity block 110, which may include fourth generation long term evolution (4G LTE) connectivity, unlicensed Wi-Fi connectivity, USB connectivity, Bluetooth® connectivity, and the like. In addition, a multimedia processor 112 in combination with a display 130 may, for example, classify and categorize semantic keypoints of objects in an area of interest, according to the display 130 illustrating a view of a vehicle. In some aspects, the NPU 108 may be implemented in the CPU 102, DSP 106, and/or GPU 104. The SOC 100 may further include a sensor processor 114, image signal processors (ISPs) 116, and/or navigation 120, which may, for instance, include a global positioning system (GPS).

The SOC 100 may be based on an Advanced Risk Machine (ARM) instruction set or the like. In another aspect of the present disclosure, the SOC 100 may be a server computer in communication with the ego vehicle 150. In this arrangement, the ego vehicle 150 may include a processor and other features of the SOC 100. In this aspect of the present disclosure, instructions loaded into a processor (e.g., CPU 102) or the NPU 108 of the ego vehicle 150 may include code for monocular visual odometry in an image captured by the sensor processor 114. The instructions loaded into a processor (e.g., CPU 102) may also include code for planning and control (e.g., intention prediction of the ego vehicle) in response to detecting ego-motion of the ego vehicle based on an image captured by the sensor processor 114.

FIG. 2 is a block diagram illustrating a software architecture 200 that may modularize functions for multi-camera monocular depth estimation based on pose averaging, according to aspects of the present disclosure. Using the architecture, a planner/controller application 202 is designed to cause various processing blocks of a system-on-a-chip (SOC) 220 (for example a CPU 222, a DSP 224, a GPU 226, and/or an NPU 228) to perform supporting computations during run-time operation of the planner/controller application 202.

The planner/controller application 202 may be configured to call functions defined in a user space 204 that may, for example, provide for multi-camera monocular depth estimation based on pose averaging in frames of a video stream captured by a monocular camera of an ego vehicle. The planner/controller application 202 may make a request to compile program code associated with a library defined in a multi-camera pose averaging application programming interface (API) 206 for learning a multi-camera pose. The planner/controller application 202 may make a request to compile program code associated with a library defined in a monocular depth estimation API 207 for the task of improving multi-camera monocular depth estimation to generate a scale-aware full surround mono-depth (FSM) 360° point cloud from a video stream captured by a multi-camera rig of an autonomous agent. The planner/controller application 202 may configure a vehicle control action by planning a trajectory of the ego vehicle according to the FSM 360° point cloud of the scene surrounding the ego vehicle.

A run-time engine 208, which may be compiled code of a runtime framework, may be further accessible to the planner/controller application 202. The planner/controller application 202 may cause the run-time engine 208, for example, to perform tracking of objects in subsequent frames of a multi-camera video stream. When an object is detected within a predetermined distance of the ego vehicle, the run-time engine 208 may in turn send a signal to an operating system 210, such as a Linux Kernel 212, running on the SOC 220. The operating system 210, in turn, may cause a computation to be performed on the CPU 222, the DSP 224, the GPU 226, the NPU 228, or some combination thereof. The CPU 222 may be accessed directly by the operating system 210, and other processing blocks may be accessed through a driver, such as drivers 214-218 for the DSP 224, for the GPU 226, or for the NPU 228. In the illustrated example, the deep neural network may be configured to run on a combination of processing blocks, such as the CPU 222 and the GPU 226, or may be run on the NPU 228, if present.

FIG. 3 is a diagram illustrating an example of a hardware implementation for a multi-camera monocular depth estimation system 300 to form a full surround mono-depth (FSM) 360° point cloud using multi-camera pose averaging, according to aspects of the present disclosure. The multi-camera monocular depth estimation system 300 may be configured for planning and control of an ego vehicle using an FSM 360° point cloud from frames of multi-camera video stream captured during operation of a car 350. The multi-camera monocular depth estimation system 300 may be a component of a vehicle, a robotic device, or other device. For example, as shown in FIG. 3 , the multi-camera monocular depth estimation system 300 is a component of the car 350. Aspects of the present disclosure are not limited to the multi-camera monocular depth estimation system 300 being a component of the car 350, as other devices, such as a bus, motorcycle, or other like vehicle, are also contemplated for using the multi-camera monocular depth estimation system 300. The car 350 may be autonomous or semi-autonomous.

The multi-camera monocular depth estimation system 300 may be implemented with an interconnected architecture, represented generally by an interconnect 308. The interconnect 308 may include any number of point-to-point interconnects, buses, and/or bridges depending on the specific application of the multi-camera monocular depth estimation system 300 and the overall design constraints of the car 350. The interconnect 308 links together various circuits including one or more processors and/or hardware modules, represented by a sensor module 302, an ego perception module 310, a processor 320, a computer-readable medium 322, communication module 324, a locomotion module 326, a location module 328, a planner module 330, and a controller module 340. The interconnect 308 may also link various other circuits such as timing sources, peripherals, voltage regulators, and power management circuits, which are well known in the art, and therefore, will not be described any further.

The multi-camera monocular depth estimation system 300 includes a transceiver 332 coupled to the sensor module 302, the ego perception module 310, the processor 320, the computer-readable medium 322, the communication module 324, the locomotion module 326, the location module 328, a planner module 330, and the controller module 340. The transceiver 332 is coupled to an antenna 334. The transceiver 332 communicates with various other devices over a transmission medium. For example, the transceiver 332 may receive commands via transmissions from a user or a remote device. As discussed herein, the user may be in a location that is remote from the location of the car 350. As another example, the transceiver 332 may transmit the FSM 360° point cloud and/or planned actions from the ego perception module 310 to a server (not shown).

The multi-camera monocular depth estimation system 300 includes the processor 320 coupled to the computer-readable medium 322. The processor 320 performs processing, including the execution of software stored on the computer-readable medium 322 to provide FSM 360° point cloud functionality based on multi-camera pose, according to aspects of the present disclosure. The software, when executed by the processor 320, causes the multi-camera monocular depth estimation system 300 to perform the various functions described for ego vehicle perception based on monocular depth estimation using pose averaging from video captured by a multi-camera rig of an ego vehicle, such as the car 350, or any of the modules (e.g., 302, 310, 324, 326, 328, 330, and/or 340). The computer-readable medium 322 may also be used for storing data that is manipulated by the processor 320 when executing the software.

The sensor module 302 may obtain images via different sensors, such as a first sensor 304 and a second sensor 306. The first sensor 304 may be a vision sensor (e.g., a stereoscopic camera or a red-green-blue (RGB) camera) for capturing 2D RGB images. The second sensor 306 may be a ranging sensor, such as a light detection and ranging (LIDAR) sensor or a radio detection and ranging (RADAR) sensor. Of course, aspects of the present disclosure are not limited to the aforementioned sensors, as other types of sensors (e.g., thermal, sonar, and/or lasers) are also contemplated for either of the first sensor 304 or the second sensor 306.

The images of the first sensor 304 and/or the second sensor 306 may be processed by the processor 320, the sensor module 302, the ego perception module 310, the communication module 324, the locomotion module 326, the location module 328, and the controller module 340. In conjunction with the computer-readable medium 322, the images from the first sensor 304 and/or the second sensor 306 are processed to implement the functionality described herein. In one configuration, detected 3D object information captured by the first sensor 304 and/or the second sensor 306 may be transmitted via the transceiver 332. The first sensor 304 and the second sensor 306 may be coupled to the car 350 or may be in communication with the car 350.

The location module 328 may determine a location of the car 350. For example, the location module 328 may use a global positioning system (GPS) to determine the location of the car 350. The location module 328 may implement a dedicated short-range communication (DSRC)-compliant GPS unit. A DSRC-compliant GPS unit includes hardware and software to make the car 350 and/or the location module 328 compliant with one or more of the following DSRC standards, including any derivative or fork thereof: EN 12253:2004 Dedicated Short-Range Communication—Physical layer using microwave at 5.9 GHz (review); EN 12795:2002 Dedicated Short-Range Communication (DSRC)—DSRC Data link layer: Medium Access and Logical Link Control (review); EN 12834:2002 Dedicated Short-Range Communication—Application layer (review); EN 13372:2004 Dedicated Short-Range Communication (DSRC)—DSRC profiles for RTTT applications (review); and EN ISO 14906:2004 Electronic Fee Collection—Application interface.

A DSRC-compliant GPS unit within the location module 328 is operable to provide GPS data describing the location of the car 350 with space-level accuracy for accurately directing the car 350 to a desired location. For example, the car 350 is driving to a predetermined location and desires partial sensor data. Space-level accuracy means the location of the car 350 is described by the GPS data sufficient to confirm a location of the parking space of the car 350. That is, the location of the car 350 is accurately determined with space-level accuracy based on the GPS data from the car 350.

The communication module 324 may facilitate communications via the transceiver 332. For example, the communication module 324 may be configured to provide communication capabilities via different wireless protocols, such as Wi-Fi, 5G new radio (NR), long term evolution (LTE), 3G, etc. The communication module 324 may also communicate with other components of the car 350 that are not modules of the multi-camera monocular depth estimation system 300. The transceiver 332 may be a communications channel through a network access point 360. The communications channel may include DSRC, LTE, LTE-D2D, mmWave, Wi-Fi (infrastructure mode), Wi-Fi (ad-hoc mode), visible light communication, TV white space communication, satellite communication, full-duplex wireless communications, or any other wireless communications protocol such as those mentioned herein.

In some configurations, the network access point 360 includes Bluetooth® communication networks or a cellular communications network for sending and receiving data, including via short messaging service (SMS), multimedia messaging service (MMS), hypertext transfer protocol (HTTP), direct data connection, wireless application protocol (WAP), e-mail, DSRC, full-duplex wireless communications, mmWave, Wi-Fi (infrastructure mode), Wi-Fi (ad-hoc mode), visible light communication, TV white space communication, and satellite communication. The network access point 360 may also include a mobile data network that may include third generation (3G), fourth generation (4G), fifth generation (5G), long term evolution (LTE), LTE-vehicle-to-everything (V2X), LTE-driver-to-driver (D2D), Voice over LTE (VoLTE), or any other mobile data network or combination of mobile data networks. Further, the network access point 360 may include one or more IEEE 802.11 wireless networks.

The multi-camera monocular depth estimation system 300 also includes the planner module 330 for planning a selected route/action (e.g., collision avoidance) of the car 350 and the controller module 340 to control the locomotion of the car 350. The controller module 340 may perform the selected action via the locomotion module 326 for autonomous operation of the car 350 along, for example, a selected route. In one configuration, the planner module 330 and the controller module 340 may collectively override a user input when the user input is expected (e.g., predicted) to cause a collision according to an autonomous level of the car 350. The modules may be software modules running in the processor 320, resident/stored in the computer-readable medium 322, and/or hardware modules coupled to the processor 320, or some combination thereof.

The National Highway Traffic Safety Administration (NHTSA) has defined different “levels” of autonomous vehicles (e.g., Level 0, Level 1, Level 2, Level 3, Level 4, and Level 5). For example, if an autonomous vehicle has a higher level number than another autonomous vehicle (e.g., Level 3 is a higher level number than Levels 2 or 1), then the autonomous vehicle with a higher level number offers a greater combination and quantity of autonomous features relative to the vehicle with the lower level number. These different levels of autonomous vehicles are described briefly below.

Level 0: In a Level 0 vehicle, the set of advanced driver assistance system (ADAS) features installed in a vehicle provide no vehicle control, but may issue warnings to the driver of the vehicle. A vehicle which is Level 0 is not an autonomous or semi-autonomous vehicle.

Level 1: In a Level 1 vehicle, the driver is ready to take driving control of the autonomous vehicle at any time. The set of ADAS features installed in the autonomous vehicle may provide autonomous features such as: adaptive cruise control (ACC); parking assistance with automated steering; and lane keeping assistance (LKA) type II, in any combination.

Level 2: In a Level 2 vehicle, the driver is obliged to detect objects and events in the roadway environment and respond if the set of ADAS features installed in the autonomous vehicle fail to respond properly (based on the driver's subjective judgement). The set of ADAS features installed in the autonomous vehicle may include accelerating, braking, and steering. In a Level 2 vehicle, the set of ADAS features installed in the autonomous vehicle can deactivate immediately upon takeover by the driver.

Level 3: In a Level 3 ADAS vehicle, within known, limited environments (such as freeways), the driver can safely turn their attention away from driving tasks, but must still be prepared to take control of the autonomous vehicle when needed.

Level 4: In a Level 4 vehicle, the set of ADAS features installed in the autonomous vehicle can control the autonomous vehicle in all but a few environments, such as severe weather. The driver of the Level 4 vehicle enables the automated system (which is comprised of the set of ADAS features installed in the vehicle) only when it is safe to do so. When the automated Level 4 vehicle is enabled, driver attention is not required for the autonomous vehicle to operate safely and consistent within accepted norms.

Level 5: In a Level 5 vehicle, other than setting the destination and starting the system, no human intervention is involved. The automated system can drive to any location where it is legal to drive and make its own decision (which may vary based on the jurisdiction where the vehicle is located).

A highly autonomous vehicle (HAV) is an autonomous vehicle that is Level 3 or higher. Accordingly, in some configurations the car 350 is one of the following: a Level 0 non-autonomous vehicle; a Level 1 autonomous vehicle; a Level 2 autonomous vehicle; a Level 3 autonomous vehicle; a Level 4 autonomous vehicle; a Level 5 autonomous vehicle; and an HAV.

The ego perception module 310 may be in communication with the sensor module 302, the processor 320, the computer-readable medium 322, the communication module 324, the locomotion module 326, the location module 328, the planner module 330, the transceiver 332, and the controller module 340. In one configuration, the ego perception module 310 receives sensor data from the sensor module 302. The sensor module 302 may receive the sensor data from the first sensor 304 and the second sensor 306. According to aspects of the present disclosure, the ego perception module 310 may receive sensor data directly from the first sensor 304 or the second sensor 306 to perform monocular ego-motion estimation from images captured by the first sensor 304 or the second sensor 306 of the car 350.

Self-supervised learning enables the extraction of information from arbitrarily large amounts of data. Depth is particularly suitable for this task because depth is trained using geometry constraints, such as structure-from-motion in raw video sequences. Unfortunately, the photometric loss commonly used when training such networks is scale-agnostic. That is, scale-agnostic photometric loss is limited to generating models accurate up to an unknown factor. This prevents self-supervised depth models (albeit highly accurate) from being applicable in a wide range of downstream tasks where a metric scale is specified, such as 3D object detection.

Multi-camera approaches to self-supervised depth and ego-motion are currently restricted to a stereo setting, with rectified images that enable disparity learning, which is converted to depth through a known baseline. Although methods are proposed that combine stereo and monocular self-supervision, directly regressing depth from stereo pairs assumes the availability of highly-overlapping images from available datasets and/or accurate velocity estimates. The availability of extrinsics is an easier specification than accurate velocity estimates or highly overlapping images because extrinsics are obtained off-line with off-the-shelf calibration techniques that achieve high precision. In addition, intrinsic information is readily available by using similar calibration techniques.

A known limitation of self-supervised depth and ego-motion models is their inability to generate metrically accurate models in multi-camera settings. Current self-supervised depth and pose architectures are either monocular or stereo in nature, precluding the ability to estimate camera motion independently from multiple cameras. Monocular networks use a single camera, and pose estimation is performed by learning frame-to-frame motion. By contrast, stereo methods train on images pairs acquired at the same time-step without considering a temporal context. Instead, stereo methods use a known baseline between cameras and, therefore, forego pose calculation pose between the images.

Aspects of the present disclosure are directed to facilitating consistent multi-camera learning of camera pose. Current monocular depth estimation architectures are generally composed of two parts: a depth network (e.g., that predicts a depth map from a single image) and a pose network (e.g. that predicts a camera pose/motion between two successive images). These aspects of the present disclosure involve an architecture that learns consistent pose from multiple cameras attached to an autonomous vehicle (e.g., a multi-camera rig or any other robot rig). In one aspect of the present disclosure, a method takes successive frames from all cameras as input. For example, these camera frames may or may not be overlapping, as long as the extrinsics between the cameras are known. In this example, N independent pose predictions are provided for N cameras. Nevertheless, these poses are essentially estimating the same motion, just in a different coordinate frame.

Some aspects of the present disclosure project predicted transformations into a common coordinate frame. For example, a common coordinate frame may include car centric coordinates with respect to the center of the rear axle. Once the predicted transformation is projected into the common coordinate frame, a penalty is added for any individual camera pose prediction deviating from the average of the pose predictions from each of the cameras. In addition, rotation and translation are handled independently to account for the different scale between the two predictions. This constraint improves both the pose prediction and the depth prediction for each of the cameras of the multi-camera rig.

As shown in FIG. 3 , the ego perception module 310 includes a multi-camera photometric loss module 312, a multi-camera pose consistency constraint (PCC) loss module 314, a depth and ego-motion training module 316, and a full surround mono-depth (FSM) 360° point cloud prediction module 318. The multi-camera photometric loss module 312, the multi-camera PCC loss module 314, and the depth and ego-motion training module 316 may be components of a same or different artificial neural network. For example, the artificial neural network is a convolutional neural network (CNN) communicably coupled to a multi-camera rig. The ego perception module 310 receives a data stream from the first sensor 304 and/or the second sensor 306. The data stream may include a 2D red-green-blue (RGB) image from the first sensor 304 and/or the second sensor 306. The data stream may include multiple frames, such as image frames. In one configuration, the first sensor 304 and the second sensor 306 are replaced with a multi-camera rig to capture 2D RGB images.

The ego perception module 310 is configured to perform multi-camera monocular depth estimation based on pose averaging using the multi-camera PCC loss module 314 and the depth and ego-motion training module 316 for the task of generating a 360° point cloud using the FSM 360° point cloud prediction module 318. In one configuration, the multi-camera photometric loss module 312 generates a photometric loss by warping an image obtained by one of multiple cameras onto a viewpoint of another camera of a multi-camera rig of the car 350.

According to aspects of the present disclosure, the multi-camera photometric loss is provided to the multi-camera PCC loss module 314 to adjust the multi-camera photometric loss according to the PCC loss associated with the multi-camera rig of the ego vehicle to form a PCC photometric loss. The PCC photometric loss is provided to the depth and ego-motion training module 316 to train a depth destination model and an ego-motion estimation model. The multi-camera monocular depth estimation system 300 may be configured for planning and control of an ego vehicle using an FSM 360° point cloud from frames of a multi-camera rig during operation of the ego vehicle, for example, as shown in FIG. 4 .

FIG. 4 illustrates an example of an ego vehicle 450 (e.g., the car 350) in an environment 400, according to aspects of the present disclosure. As shown in FIG. 4 , the ego vehicle 450 is traveling on a road 410. A first vehicle 404 (e.g., other agent) may be ahead of the ego vehicle 450, and a second vehicle 416 may be adjacent to the ego vehicle 450. In this example, the ego vehicle 450 may include a 2D camera 456, such as a 2D red-green-blue (RGB) camera, and a second sensor 458. The second sensor 458 may be another RGB camera or another type of sensor, such as ultrasound, radio detection and ranging (RADAR), and/or light detection and ranging (LIDAR), as shown by reference number 462. Additionally, or alternatively, the ego vehicle 450 may include one or more additional sensors. For example, the additional sensors may be side facing and/or rear facing sensors.

In one configuration, the 2D camera 456 captures a 2D image that includes objects in the field of view 460 of the 2D camera 456. The second sensor 458 may generate one or more output streams. The 2D image captured by the 2D camera 456 includes a 2D image of the first vehicle 404, as the first vehicle 404 is in the field of view 460 of the 2D camera 456. A field of view 470 of the second sensor 458 is also shown.

The information obtained from the 2D camera 456 and the second sensor 458 may be used to navigate the ego vehicle 450 along a route when the ego vehicle 450 is in an autonomous mode. The 2D camera 456 and the second sensor 458 may be powered from electricity provided from the battery (not shown) of the ego vehicle 450. The battery may also power the motor of the ego vehicle 450. The information obtained from the 2D camera 456 and the second sensor 458 may be used to generate a 3D representation of an environment.

Self-supervised learning enables the extraction of information from arbitrarily large amounts of data. Depth is particularly suitable for this task because depth is trained using geometry constraints, such as structure-from-motion in raw video sequences. Multi-camera approaches to self-supervised depth and ego-motion are currently restricted to a stereo setting, with rectified images that enable disparity learning, which is converted to depth through a known baseline. Although methods are proposed that combine stereo and monocular self-supervision, directly regressing depth from stereo pairs assumes the availability of highly-overlapping images from available datasets and/or accurate velocity estimates.

A known limitation of self-supervised depth and ego-motion models is their inability to generate metrically accurate models in multi-camera settings, such as shown in FIG. 4 . Current self-supervised depth and pose architectures are either monocular or stereo in nature, precluding the ability to estimate camera motion independently from multiple cameras. Monocular networks use a single camera, and pose estimation is performed by learning frame-to-frame motion. By contrast, stereo methods train on images pairs acquired at the same time-step without considering a temporal context. Instead, stereo methods use a known baseline between cameras and, therefore, forego pose calculation pose between the images.

Aspects of the present disclosure are directed to facilitating consistent multi-camera learning of camera pose. Current monocular depth estimation architectures are generally composed of two parts: a depth network (e.g., that predicts a depth map from a single image) and a pose network (e.g., that predicts a camera pose/motion between two successive images). These aspects of the present disclosure involve an architecture that learns consistent pose from multiple cameras attached to an autonomous vehicle (e.g., a multi-camera rig or any other robot rig). In one aspect of the present disclosure, a method takes successive frames from all cameras as input. For example, these camera frames may or may not be overlapping, as long as the extrinsics between the cameras are known. In this example, N independent pose predictions are provided for N cameras. Nevertheless, these poses are essentially estimating the same motion, just in a different coordinate frame while providing a full 360° coverage, for example, as shown in FIG. 5 .

FIG. 5 is a diagram illustrating a full surround mono-depth (FSM) 360° point cloud 500 generated using self-supervised learning of scale-aware and consistent depth and ego-motion networks in a wide-baseline 360° multi-camera setting, according to aspects of the present disclosure. Aspects of the present disclosure leverage cross-camera temporal contexts via spatio-temporal photometric constraints to increase the amount of overlap between cameras using a system's ego-motion. By exploiting known extrinsics between cameras, and enforcing pose consistency constraints to ensure the cameras follow the same rigid body motion, scale-aware models are learned without ground-truth depth or ego-motion labels.

In this configuration, the FSM 360° point cloud 500 is generated from images 510 (510-1, 510-2, 510-3, 510-4, 510-5, and 510-6) that capture a scene surrounding an ego vehicle. Although there is minimal overlap between the cameras used to capture the images 510, cross-camera temporal contexts are leveraged via spatio-temporal photometric constraints to increase the amount of overlap between the cameras based on ego-motion. In these aspects of the present disclosure, the FSM 360° point cloud 500 is generated by running depth and ego-motion networks trained using a multi-camera photometric loss of a multi-camera rig of an ego vehicle, for example, as shown in FIG. 6 .

FIG. 6 is a diagram illustrating a distribution of a multi-camera rig of an ego vehicle, with each camera pointing in a different direction to generate a 360° view of an environment surrounding the ego vehicle, according to aspects of the present disclosure. In this example, the multi-camera rig 600 is a six-camera rig that captures the images 510 of FIG. 5 . This example illustrates red-green-blue (RGB) images (e.g., the images 510) captured by each of the cameras 610 (e.g., 610-1, 610-2, 610-3, 610-4, 610-5, and 610-6) of a multi-camera rig 600 of the ego vehicle 450.

In these aspects of the present disclosure, the cameras 610 capture the images 510 in a particular time-step, including temporal contexts (e.g., a forward frame). In a self-supervised depth and ego-motion setting, both the depth network and the ego-motion network of the scale-aware depth estimation model and the ego-motion estimation model are trained by reducing a multi-camera photometric loss, as shown in FIG. 3 . In these aspects of the present disclosure, the multi-camera photometric loss is obtained by warping one of the images 510 obtained by one of the cameras 610 onto a viewpoint of another one of the images 510 obtained by one of the cameras 610 of the multi-camera rig 600.

These aspects of the present disclosure first introduce a base case for monocular self-supervised depth and ego-motion learning, and then extend the description to the proposed multi-camera setting. Self-supervised depth and ego-motion architectures may be composed of a depth network that produces depth maps {circumflex over (D)}_(t) for a target image I_(t), as well as a pose network that predicts a relative pose for pairs of target t and context c frames. This pose prediction is a rigid transformation according to the following equation:

$\begin{matrix} {{\hat{X}}_{t\rightarrow c} = {\begin{pmatrix} {\hat{R}}_{t\rightarrow c} & {\hat{t}}_{t\rightarrow c} \\ 0 & 1 \end{pmatrix} \in {{SE}(3)}}} & (1) \end{matrix}$

These aspects of the present disclosure jointly train depth and ego-motion networks by reducing a re-projection error between an actual target image I_(t) and a synthesized image Î_(t). The synthesized image Î_(t) is subsequently obtained by projecting pixels from a context image I_(c) (usually preceding or following a target image I_(t) in a sequence) onto the target image I_(t). The photometric re-projection loss is composed of the following structure similarity (SSIM) metric and an L1 loss term:

$\begin{matrix} {{\mathcal{L}_{p}\left( {I_{t},{\hat{I}}_{t}} \right)} = {{\alpha\frac{1 - {{SSIM}\left( {I_{t},{\hat{I}}_{t}} \right)}}{2}} + {\left( {1 - \alpha} \right){{I_{t} - {\hat{I}}_{t}}}}}} & (2) \end{matrix}$

Aspects of the present disclosure are directed to reducing the photometric loss according to Equation (2) during training. Reducing the photometric loss according to Equation (2) constrains the depth and pose networks to learn the tasks of depth and pose estimation as a by-product of the geometry of the problem. In particular, image warping involves precise depth and pose knowledge. As a result, reducing the photometric loss is achieved when both the depth and pose networks are generating accurate results. Unfortunately, because the photometric loss is scale-agnostic, the depth and pose networks produce predictions that are accurate up to a factor.

In some aspects of the present disclosure, the target image I_(t) is view synthesized using a spatial transformer network (STN) via grid sampling in combination with bilinear interpolation. This view synthesis operation is thus fully differentiable, enabling gradient back-propagation for end-to-end training. A pixel warping operation may be defined according to the following equation:

{circumflex over (P)} _(t)=π(R _(t→c)ϕ(p _(t) ,d _(t) ,K)+t _(t→c) ,K)   (3)

where ϕ(p, d, K)=P is responsible for the non-projection of an image pixel in homogeneous coordinates p to a 3D point P given its depth value d. Conversely, π(P, K)=p projects a 3D point back onto the image plane as a pixel. Both operations involve knowledge of the camera parameters, which for a standard pinhole model are defined by a 3×3 intrinsics matrix K. For convenience, in the remainder of this specification the following abbreviates are used: ϕi(p, d)=ϕ(p, d, K_(i)) and π_(i)(P)=π(P, K_(i)).

In Equation (3), R represents a rotation matrix between a target camera and context camera, t is the translation vector, p is a pixel with coordinates (u, v) within the image from the context camera, d is the depth value of the pixel, and π and ϕ are the reconstruction and projection operations. These operations are dependent on the camera model, and for the standard pinhole geometry take the following form:

$\begin{matrix} {{s\begin{bmatrix} u \\ v \\ 1 \end{bmatrix}} = {{\begin{bmatrix} f_{x} & 0 & c_{x} \\ 0 & f_{y} & c_{y} \\ 0 & 0 & 1 \end{bmatrix}\begin{bmatrix} r_{11} & r_{12} & r_{13} & t_{1} \\ r_{21} & r_{22} & r_{23} & t_{2} \\ r_{31} & r_{32} & r_{33} & t_{3} \end{bmatrix}}\begin{bmatrix} X \\ Y \\ Z \\ 1 \end{bmatrix}}} & (4) \end{matrix}$

According to Equation (4), (u, v, 1) are pixel coordinates, (X, Y, Z, 1) are 3D coordinates, and “s” is a scale factor that corresponds to the depth value at that pixel. In this example, two matrices are shown: (1) an intrinsic matrix K; and (2) a transformation matrix. The intrinsic matrix K, contains focal lengths (fx, fy) and principal points (c_(x), c_(y)). In addition, the transformation matrix positions the camera in a given global frame of reference.

Multi-camera approaches to self-supervised depth and ego-motion are currently restricted to the stereo setting. Although methods have been proposed that combine stereo and monocular self-supervision, directly regressing depth also from stereo pairs, these methods assume the availability of highly-overlapping images. Aspects of the present disclosure differ from the stereo setting by exploiting small overlaps between cameras with arbitrary locations. These aspects of the present disclosure both improve individual camera performance and generate scale-aware models from known extrinsics. Furthermore, multi-camera rigs, such as the multi-camera rig 600 with a low overlap are available. That is, the multi-camera rig 600 provides autonomous driving with a cost-effective solution to 360° vision, as follows.

Let C^(i) and C^(j) be two cameras with extrinsics X^(i) and X^(j). Denoting the relative extrinsics as X^(i→j), Equation (3) is used to warp the images from the two cameras:

{circumflex over (p)} ^(i)=π(R ^(i→j)ϕ(p ^(i) ,{circumflex over (d)} ^(i))+t ^(i→j))   (5)

It is noted that the Equation (5) is purely spatial, since it warps images between different cameras taken at the same time-step. Conversely, Equation (3) is purely temporal, since it is only concerned with warping images from the same camera taken at different time-steps. Therefore, for any given camera Ci at a time-step t, a context image can be either temporal (e.g., from adjacent frames t−1 and t+1) or spatial (e.g., from any camera j that overlaps with the camera Ci). The concept of “context image” in self-supervised learning is further generalized to include temporal contexts from other overlapping cameras. According to aspects of the present disclosure, combining spatial and temporal contexts in a context image is performed by warping images between different cameras taken at different time-steps using a composition of known extrinsics with predicted ego-motion:

{circumflex over (p)} _(t) ^(i)=π(R ^(i→j)({circumflex over (R)} _(t→c) ^(j)ϕ(p _(t) ^(j) ,d _(t) ^(j))+{circumflex over (t)} _(t→c) ^(j))+t ^(i→j))   (6)

FIG. 7 is a diagram 700 illustrating multi-camera spatio-temporal transformation matrices to enable warping of images between different cameras taken at different time-steps, according to aspects of the present disclosure. In these examples, transformations are performed between target images I_(t) (e.g., current frames) and context images I_(c) (e.g., adjacent frames). For example, spatial transformations (X^(i→1)) are obtained from known extrinsics, and temporal transformations ({circumflex over (X)}_(t→c) ^(i)) are obtained from a pose network. The target images include a first target image 710, a second target image 720, and a third target image 730. In addition, the context images include a first context image 740, a second context image 750, and a third context image 760.

In this example, a first spatial transformation 722 is performed between the second target image 720 and the first target image 710, and a second spatial transformation 732 is performed between the third target image 730 and the first target image 710. In addition, a first temporal transformation 712 is performed between the first target image 710 and the first context image 740. In addition, a second temporal transformation 724 is performed between the second target image 720 and the second context image 750, and a third temporal transformation 734 is performed between the third target image 730 and the third context image 760. Combining spatial and temporal contexts is performed by warping images between different cameras taken at different time-steps using a composition of known extrinsics with predicted ego-motion, for example, as shown in FIG. 8 .

FIG. 8 is a drawing illustrating image warping for spatial and temporal contexts to generate a multi-camera photometric loss, according to aspects of the present disclosure. In the drawing 800 of FIG. 8 , a first row 810 illustrates input RGB images, which are composed of the images 510 (510-1, 510-2, 510-3, 510-4, 510-5, and 510-6) in a clockwise direction of FIG. 5 that capture the scene surrounding the ego vehicle 450. In this example, a fifth image 510-5 is shown in a first column, a first image 510-1 is shown in a second column, and a sixth image 510-6 is shown in a third column of the drawing 800. In addition, a second image 510-2 is shown in a fourth column, a fourth image 510-4 is shown in a fifth column, and a third image 510-3 is shown in a sixth and final column of the drawing 800.

In these aspects of the present disclosure, a second row 820 illustrates warped versions of the images 510 according to a temporal context of Equation (3), and a third row 830 illustrates a corresponding photometric loss according to Equation (1). A fourth row 840 illustrates synthesized views from surrounding cameras according to a spatial context of Equation (5), and a fifth row 850 illustrates a corresponding photometric loss according to Equation (1) based on a purely spatial context. A sixth row 860 illustrates a spatial-temporal photometric loss according to Equation (6). Aspects of the present disclosure generate larger overlap between images, as well as a smaller residual photometric error for optimization by leveraging temporal contexts during cross-camera photometric warping.

In these aspects of the present disclosure, FIG. 8 shows examples of warped images and corresponding photometric losses based on the images 510 of FIG. 5 . Particularly, the fifth row 850 and the sixth row 860 show examples of multi-camera photometric losses using purely spatial contexts (Equation (5)) and spatio-temporal contexts (Equation (6)). As shown in the drawing 800 of FIG. 8 , spatio-temporal contexts (STC) promote a larger overlapping area between cameras and smaller residual photometric loss, due to occlusions and changes in brightness and viewpoint. This improved photometric loss beneficially leads to better self-supervision for depth and ego-motion learning in a multi-camera setting.

These aspects of the present disclosure, however, consider the photometric loss between cameras in a same time-step, in addition to between the same camera in different time-steps. As a result, this spatio-temporal context information is encoded into the photometric loss definition. Therefore, this spatio-temporal context information is learned as part of an optimization process of the depth and pose networks. It should be recognized that an overlap between the cameras 610 in the same time-step is much smaller than the overlap between the same camera in subsequent time-steps, as shown in the second row 820 and the fourth row 840 of FIG. 8 . Nevertheless, this smaller amount of overlap is sufficient to constrain learning and generate scale-aware models without explicit supervision, in the form of ground-truth depth maps or pose values.

TABLE 1 Multi-camera photometric loss (Multi) vs. Standard photometric loss (Mono) Lower is better ↓ Higher is better ↑ Camera Abs. Rel Sqr. Rel RMSE RMSE_(Log) SILog δ < 1.25 δ < 1.25² δ < 1.25³ Mono 01 0.143 3.133 13.875 0.237 23.029 0.828 0.935 0.970 (Shared) 05 0.227 3.624 13.545 0.365 30.685 0.623 0.840 0.917 06 0.253 4.197 13.324 0.400 33.375 0.582 0.821 0.905 07 0.264 4.103 14.038 0.419 31.827 0.509 0.778 0.890 08 0.263 3.856 12.739 0.419 34.001 0.530 0.795 0.896 09 0.236 4.235 16.729 0.386 30.137 0.558 0.814 0.911 Multi 01 0.138 2.924 14.041 0.232 22.557 0.826 0.937 0.971 (Shared) 05 0.218 3.363 13.172 0.351 30.966 0.655 0.848 0.923 06 0.241 3.896 12.827 0.377 32.893 0.621 0.835 0.914 07 0.249 3.761 13.289 0.384 32.133 0.580 0.818 0.908 08 0.257 3.549 12.096 0.393 33.719 0.568 0.821 0.910 09 0.205 3.629 15.419 0.334 30.211 0.668 0.865 0.936

Table 1 illustrates a comparison of multi-camera photometric loss relative to standard photometric loss. That is, Table 1 illustrates depth estimate results using the multi-camera photometric loss (“Multi”) relative to standard photometric loss (“Mono”). In Table 1, the results are scaled and do not involve correction at test time. As illustrated by the results of Table 1, the corresponding point clouds are consistent with each other (e.g., have a same metric scale), producing a single point cloud that covers an entire area surrounding the ego vehicle.

A known limitation of self-supervised depth and ego-motion models is their inability to generate metrically accurate models in multi-camera settings. Some aspects of the present disclosure project predicted transformations into a common coordinate frame. For example, a common coordinate frame may include car centric coordinates with respect to the center of the rear axle. Once the predicted transformation is projected into the common coordinate frame, a penalty is added for any individual camera pose prediction deviating from the average of the pose predictions from each of the cameras. In addition, rotation and translation are handled independently to account for the different scale between the two predictions. This constraint improves both the pose prediction and the depth prediction for each of the cameras 610 of the multi-camera rig 600 of the ego vehicle 450 shown in FIG. 6 .

In particular, current self-supervised depth and pose architectures are either monocular or stereo in nature, precluding the ability to estimate camera motion independently from multiple cameras. Monocular networks use a single camera, and pose estimation is performed by learning frame-to-frame motion. By contrast, stereo methods train on images pairs acquired at the same time-step without considering temporal context. Instead, stereo methods use a known baseline between cameras and, therefore, forego pose calculation pose between the images.

By contrast, aspects of the present disclosure describe a learned multi-camera pose network that exploits cross-camera consistencies by focusing on both partial and temporal aspects. As described, spatial aspects refer to consistencies between cameras in the same time-step (e.g., cameras i and j). In addition, temporal aspects refer to consistencies between the same camera in different time-steps (e.g., camera i at time-step t and t+1). In these aspects of the present disclosure, spatial constraints are exploited to improve the temporal calculation of camera motion over time. In addition, a combination of known extrinsics are used with predicted pose to go from any frame of reference to any frame of reference (for example, from camera i at time-step t−1 to camera j at time-step t+1).

Beyond cross-camera constraints due to image overlap, there are also natural pose constraints due to the fact that the cameras 610 of the multi-camera rig 600 are rigidly attached to the ego vehicle (e.g., relative camera extrinsics are constant and fixed). Specifically, the pose network is used to predict independent poses for each camera, even though they should correspond to the same transformation, just in a different coordinate frame. For a given camera i, the pose network predicts a transformation {circumflex over (X)}_(t→t+1) ^(i) from the current frame t to the subsequent frame t+1. In order to obtain predictions from different cameras that are in the same coordinate frame, this prediction is transformed to the coordinate frame of a “canonical” camera j. The {circumflex over (X)}_(t→t+1) ^(i) in camera j coordinates is denoted as {circumflex over (X)}_(t→t+1) ^(j).

To convert a predicted transformation {circumflex over (X)}_(t+1) ^(i) from the coordinate frame of camera i to camera j, the extrinsics X^(i) and X^(j) are used to generate {circumflex over (X)}_(t→t+1) ^(j) as follows:

$\begin{matrix} {{\hat{X}}_{t\rightarrow{t + 1}}^{i} = {X^{j - 1}X^{i}{\hat{X}}_{t\rightarrow{t + 1}}^{i}X^{i - 1}X^{j}}} & (7) \end{matrix}$ $\begin{matrix} {{{where}{\hat{X}}_{t\rightarrow{t + 1}}^{i}} = \begin{pmatrix} {\overset{\sim}{R}}_{t\rightarrow{t + 1}}^{i} & {\overset{\sim}{t}}_{t\rightarrow{t + 1}}^{T} \\ 0 & 1 \end{pmatrix}} & (8) \end{matrix}$

As a convention, predicted transformations are converted to the coordinate frame of the front camera C¹. Once all predictions are in the same coordinate frame, the translation vectors t and rotation matrices R are constrained to be similar across all cameras 610 of the multi-camera rig 600 of the ego vehicle 450. Translation. All predicted translation vectors are constrained to be similar to the prediction for the front camera, which generally performs best across all experiments. Defining the predicted front camera translation vector as {circumflex over (t)}_(t+1) ¹, for N cameras the translation consistency loss is given by:

$\begin{matrix} {t_{loss} = {\sum\limits_{j = 1}^{N}{{{\hat{t}}_{t + 1}^{1} - {\overset{\sim}{t}}_{t + 1}^{j}}}^{2}}} & (9) \end{matrix}$

Rotation. Similarly, other cameras are constrained to predict a rotation matrix similar to the front camera. To accomplish that, once the predictions are in the same coordinate frame they are converted to Euler angles (φ¹, θ¹, ψ¹) and the rotation consistency loss is calculated such that:

$\begin{matrix} {R_{loss} = {{\sum\limits_{j = 1}^{N}{{{\hat{\phi}}^{1} - {\overset{\sim}{\phi}}^{j}}}^{2}} + {{{\hat{\theta}}^{1} - {\overset{\sim}{\theta}}^{j}}}^{2} + {{{\hat{\psi}}^{1} - {\overset{\sim}{\psi}}^{j}}}^{2}}} & (10) \end{matrix}$

Aspects of the present disclosure trade-off between rotation loss and translation loss. For example, a trade-off between the two constraints of rotation losses and translation loss by defining pose consistency constraints (PCC) loss (L_(pcc)) as:

L _(pcc)=α_(t) t _(loss)+α_(r) R _(loss),   (11)

where α_(t) and α_(r) are weight coefficients.

As described above, a constraint is introduced to tie the pose predictions between different cameras. Aspects of the present disclosure rely on the pose estimated by the front camera as the “dominant prediction,” using the constraints described above to enforce consistency with the poses estimated for others of the cameras 610 of the multi-camera rig 600 of the ego vehicle 450. Table 2 illustrates an ablation of pose constraints. As shown in Table 2, the improvement in depth estimation is modest but non-trivial (from 0.133 to 0.130 abs. rel.). It is noted that predicting the vehicle ego-motion from the front-camera, and allowing the other cameras to “borrow” from the prediction of the front-camera, further improves a depth model performance, leading to depth gains for the front-camera as well.

TABLE 2 Ablation of Pose Constraints Method Abs Rel↓ Sq Rel↓ RMSE↓ δ_(1.25) ↑ Baseline 0.133 2.817 13.598 0.837 Translation Only 0.132 2.826 13.805 0.836 Rotation Only 0.131 2.766 13.694 0.835 Both 0.130 2.752 13.866 0.838

According to aspects of the present disclosure, the noted pose consistency constraints (PCCs) enable enforcement of learning a similar rigid motion for the cameras 610 of the multi-camera rig 600 of the ego vehicle 450 to provide significant performance improvements. These improvements may be more prominent on the side cameras, because per-frame pose estimation is more difficult in these situations due to a larger relative motion and smaller overlap. Aspects of the present disclosure may combine various improvements (masking, spatio-temporal contexts and pose consistency constraints) leading to scale-aware results that surpass median-scaled results. Aspects of the present disclosure provide an FSM that is capable of generating state-of-the-art metrically accurate models that are useful for downstream tasks in real applications.

Using cameras for 3D perception to complement or replace LIDAR scanners is an exciting new frontier for robotics. Aspects of the present disclosure extend the self-supervised learning of depth and ego-motion from monocular and stereo settings to the generalized multi-camera setting, predicting consistent and dense point clouds around the ego vehicle 450. This aspect introduces a series of key techniques that boost performance in this new setting by leveraging known extrinsics between cameras: spatial-temporal contexts, pose consistency constraints, and the effects of non-overlapping and self-occlusion photometric masking. A multi-camera monocular depth estimation using a pose averaging process based on multi-camera projection loss is further described in FIG. 9 .

FIG. 9 is a flowchart illustrating a method for multi-camera monocular depth estimation using pose averaging, according to aspects of the present disclosure. The method 900 begins at block 902, in which a multi-camera photometric loss associated with a multi-camera rig of an ego vehicle is determined. For example, FIG. 8 shows examples of warped images and corresponding photometric losses based on the images 510 of FIG. 5 . Particularly, the fifth row 850 and the sixth row 860 show examples of multi-camera photometric losses using purely spatial contexts (Equation (5)) and spatio-temporal contexts (Equation (6)).

At block 904, a multi-camera pose consistency constraint (PCC) loss associated with the multi-camera rig of the ego vehicle is determined. For example, as shown in FIG. 6 , the noted pose consistency constraints (PCCs) enable enforcement of learning a similar rigid motion for the cameras 610 of the multi-camera rig 600 of the ego vehicle 450 to provide significant performance improvements. These improvements may be more prominent on the side cameras, because per-frame pose estimation is more difficult in these situations due to a larger relative motion and smaller overlap. Aspects of the present disclosure may combine various improvements (masking, spatio-temporal contexts, and pose consistency constraints) lead to scale-aware results that surpass median-scaled results.

At block 906, the multi-camera photometric loss is adjusted according to the PCC loss to form a PCC photometric loss. For example, as shown in FIG. 6 , predicted transformations are converted to the coordinate frame of the front camera C¹. Once all predictions are in the same coordinate frame, the translation vectors t and rotation matrices R are constrained to be similar across all cameras 610 of the multi-camera rig 600 of the ego vehicle 450. In addition, a trade-off between the two constraints of rotation losses and translation loss is provided by defining pose consistency constraints (PCC) loss (L_(pcc)), according to Equation (11) as well as Equations (7)-(10).

At block 908, a multi-camera depth estimation model and an ego-motion estimation model are trained according to the multi-camera PCC photometric loss. For example, as shown in FIG. 3 , the multi-camera photometric loss of the multi-camera photometric loss module 312 is adjusted by the multi-camera PCC loss module 314 to form a multi-camera PCC photometric loss. The multi-camera PCC photometric loss is used by the depth and ego-motion training module 316 to train a multi-camera depth model and an ego-motion estimation model. These aspects of the present disclosure jointly train depth and ego-motion networks by reducing a re-projection error between an actual target image and a synthesized image. Training of the self-supervised depth estimation model and the ego-motion model may include leveraging cross-camera temporal contexts via spatio-temporal photometric constraints to increase an amount of overlap between cameras of the multi-camera rig using a predicted ego-motion of the ego vehicle.

At block 910, a 360° point cloud of a scene surrounding the ego vehicle is predicted according to the trained multi-camera depth estimation model and the ego-motion estimation model. For example, FIG. 5 is a diagram illustrating a full surround mono-depth (FSM) 360° point cloud 500 generated using self-supervised learning of scale-aware and consistent depth and ego-motion networks in a wide-baseline 360° multi-camera setting, according to aspects of the present disclosure. In this configuration, the FSM 360° point cloud 500 is generated from images 510 (510-1, 510-2, 510-3, 510-4, 510-5, and 510-6) that capture a scene surrounding an ego vehicle.

The method 900 may also include planning a vehicle control action of the ego vehicle according to the 360° point cloud of the scene surrounding the ego vehicle. For example, as shown in FIG. 3 , the multi-camera monocular depth estimation system 300 may be configured for planning and control of an ego vehicle using an FSM 360° point cloud from frames of a multi-camera rig during operation of the ego vehicle, for example, as shown in FIG. 4 .

The method 900 may determine the multi-camera photometric loss by capturing images of the scene surrounding the ego vehicle using the multi-camera rig of the ego vehicle, in which cameras of the multi-camera rig have a predetermined minimum overlap. The method 900 also includes selecting target images and context images captured by the cameras of the multi-camera rig of the ego vehicle at a same time-step and at different time-steps. The method further includes performing spatial-temporal transformations of the selected target images and the context images to determine the multi-camera photometric loss. Alternatively, the method 900 performs spatial-temporal transformations by warping target images and context images captured by different cameras of the multi-camera rig at different time-steps according to a predicted ego-motion of the ego vehicle and known extrinsics of the different cameras.

Aspects of the present disclosure extend monocular self-supervised depth and ego-motion estimation to large-baseline multi-camera rigs. These aspects of the present disclosure use generalized spatio-temporal and pose consistency constraints across all cameras, and carefully designed photometric loss masking to learn a single network generating dense, consistent, and scale-aware point clouds that cover the full 360° field of view of a LIDAR scanner. These aspects of the present disclosure provide a new scale-consistent evaluation metric more suitable to multi-camera settings. In particular, aspects of the present disclosure extend self-supervised depth and ego-motion learning to the general multi-camera setting, where cameras can have different intrinsics and minimally overlapping regions, as specified to reduce the number of cameras on a vehicle rig while providing a full 360° coverage.

In some aspects of the present disclosure, the method 900 may be performed by the system-on-a-chip (SOC) 100 (FIG. 1 ) or the software architecture 200 (FIG. 2 ) of the ego vehicle 150 (FIG. 1 ). That is, each of the elements of the method 900 may, for example, but without limitation, be performed by the SOC 100, the software architecture 200, or the processor (e.g., CPU 102) and/or other components included therein of the ego vehicle 150.

The various operations of methods described above may be performed by any suitable means capable of performing the corresponding functions. The means may include various hardware and/or software component(s) and/or module(s), including, but not limited to, a circuit, an application-specific integrated circuit (ASIC), or processor. Generally, where there are operations illustrated in the figures, those operations may have corresponding counterpart means-plus-function components with similar numbering.

As used herein, the term “determining” encompasses a wide variety of actions. For example, “determining” may include calculating, computing, processing, deriving, investigating, looking up (e.g., looking up in a table, a database or another data structure), ascertaining, and the like. Additionally, “determining” may include receiving (e.g., receiving information), accessing (e.g., accessing data in a memory), and the like. Furthermore, “determining” may include resolving, selecting, choosing, establishing, and the like.

As used herein, a phrase referring to “at least one of” a list of items refers to any combination of those items, including single members. As an example, “at least one of: a, b, or c” is intended to cover: a, b, c, a-b, a-c, b-c, and a-b-c.

The various illustrative logical blocks, modules, and circuits described in connection with the present disclosure may be implemented or performed with a processor configured according to the present disclosure, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array signal (FPGA) or other programmable logic device (PLD), discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. The processor may be a microprocessor, but, in the alternative, the processor may be any commercially available processor, controller, microcontroller, or state machine specially configured as described herein. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.

The steps of a method or algorithm described in connection with the present disclosure may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in any form of storage medium that is known in the art. Some examples of storage media may include random access memory (RAM), read-only memory (ROM), flash memory, erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, a hard disk, a removable disk, a CD-ROM, and so forth. A software module may comprise a single instruction, or many instructions, and may be distributed over several different code segments, among different programs, and across multiple storage media. A storage medium may be coupled to a processor such that the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor.

The methods disclosed herein comprise one or more steps or actions for achieving the described method. The method steps and/or actions may be interchanged with one another without departing from the scope of the claims. In other words, unless a specific order of steps or actions is specified, the order and/or use of specific steps and/or actions may be modified without departing from the scope of the claims.

The functions described may be implemented in hardware, software, firmware, or any combination thereof. If implemented in hardware, an example hardware configuration may comprise a processing system in a device. The processing system may be implemented with a bus architecture. The bus may include any number of interconnecting buses and bridges depending on the specific application of the processing system and the overall design constraints. The bus may link together various circuits including a processor, machine-readable media, and a bus interface. The bus interface may connect a network adapter, among other things, to the processing system via the bus. The network adapter may implement signal processing functions. For certain aspects, a user interface (e.g., keypad, display, mouse, joystick, etc.) may also be connected to the bus. The bus may also link various other circuits such as timing sources, peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further.

The processor may be responsible for managing the bus and processing, including the execution of software stored on the machine-readable media. Examples of processors that may be specially configured according to the present disclosure include microprocessors, microcontrollers, DSP processors, and other circuitry that can execute software. Software shall be construed broadly to mean instructions, data, or any combination thereof, whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise. Machine-readable media may include, by way of example, random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, magnetic disks, optical disks, hard drives, or any other suitable storage medium, or any combination thereof. The machine-readable media may be embodied in a computer-program product. The computer-program product may comprise packaging materials.

In a hardware implementation, the machine-readable media may be part of the processing system separate from the processor. However, as those skilled in the art will readily appreciate, the machine-readable media, or any portion thereof, may be external to the processing system. By way of example, the machine-readable media may include a transmission line, a carrier wave modulated by data, and/or a computer product separate from the device, all which may be accessed by the processor through the bus interface. Alternatively, or in addition, the machine-readable media, or any portion thereof, may be integrated into the processor, such as the case may be with cache and/or specialized register files. Although the various components discussed may be described as having a specific location, such as a local component, they may also be configured in various ways, such as certain components being configured as part of a distributed computing system.

The processing system may be configured with one or more microprocessors providing the processor functionality and external memory providing at least a portion of the machine-readable media, all linked together with other supporting circuitry through an external bus architecture. Alternatively, the processing system may comprise one or more neuromorphic processors for implementing the neuron models and models of neural systems described herein. As another alternative, the processing system may be implemented with an application-specific integrated circuit (ASIC) with the processor, the bus interface, the user interface, supporting circuitry, and at least a portion of the machine-readable media integrated into a single chip, or with one or more field-programmable gate arrays (FPGAs), programmable logic devices (PLDs), controllers, state machines, gated logic, discrete hardware components, or any other suitable circuitry, or any combination of circuits that can perform the various functions described throughout the present disclosure. Those skilled in the art will recognize how best to implement the described functionality for the processing system depending on the particular application and the overall design constraints imposed on the overall system.

The machine-readable media may comprise a number of software modules. The software modules include instructions that, when executed by the processor, cause the processing system to perform various functions. The software modules may include a transmission module and a receiving module. Each software module may reside in a single storage device or be distributed across multiple storage devices. By way of example, a software module may be loaded into RAM from a hard drive when a triggering event occurs. During execution of the software module, the processor may load some of the instructions into cache to increase access speed. One or more cache lines may then be loaded into a special purpose register file for execution by the processor. When referring to the functionality of a software module below, it will be understood that such functionality is implemented by the processor when executing instructions from that software module. Furthermore, it should be appreciated that aspects of the present disclosure result in improvements to the functioning of the processor, computer, machine, or other system implementing such aspects.

If implemented in software, the functions may be stored or transmitted over as one or more instructions or code on a non-transitory computer-readable medium. Computer-readable media include both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage medium may be any available medium that can be accessed by a computer. By way of example, and not limitation, such computer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. Additionally, any connection is properly termed a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared (IR), radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. Disk and disc, as used herein, include compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk, and Blu-ray® disc; where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Thus, in some aspects computer-readable media may comprise non-transitory computer-readable media (e.g., tangible media). In addition, for other aspects, computer-readable media may comprise transitory computer-readable media (e.g., a signal). Combinations of the above should also be included within the scope of computer-readable media.

Thus, certain aspects may comprise a computer program product for performing the operations presented herein. For example, such a computer program product may comprise a computer-readable medium having instructions stored (and/or encoded) thereon, the instructions being executable by one or more processors to perform the operations described herein. For certain aspects, the computer program product may include packaging material.

Further, it should be appreciated that modules and/or other appropriate means for performing the methods and techniques described herein can be downloaded and/or otherwise obtained by a user terminal and/or base station as applicable. For example, such a device can be coupled to a server to facilitate the transfer of means for performing the methods described herein. Alternatively, various methods described herein can be provided via storage means (e.g., RAM, ROM, a physical storage medium such as a CD or floppy disk, etc.), such that a user terminal and/or base station can obtain the various methods upon coupling or providing the storage means to the device. Moreover, any other suitable technique for providing the methods and techniques described herein to a device can be utilized.

It is to be understood that the claims are not limited to the precise configuration and components illustrated above. Various modifications, changes, and variations may be made in the arrangement, operation, and details of the methods and apparatus described above without departing from the scope of the claims. 

1. A method for multi-camera monocular depth estimation using pose averaging, comprising: adjusting a multi-camera photometric loss associated with a multi-camera rig of an ego vehicle according to a multi-camera pose consistency constraint (PCC) loss associated with the multi-camera rig of the ego vehicle to form a multi-camera PCC photometric loss; and training a multi-camera depth estimation model and an ego-motion estimation model according to the multi-camera PCC photometric loss.
 2. The method of claim 1, further comprising: capturing images of the scene surrounding the ego vehicle using the multi-camera rig of the ego vehicle, in which cameras of the multi-camera rig have a predetermined minimum overlap; selecting target images and context images captured by the cameras of the multi-camera rig of the ego vehicle at a same time-step and at different time-steps; and performing spatial-temporal transformations of the selected target images and the context images to determine the multi-camera photometric loss.
 3. The method of claim 2, in which performing the spatial-temporal transformations comprises warping target images and the context images captured by different cameras of the multi-camera rig at the different time-steps according to a predicted ego-motion of the ego vehicle and known extrinsics of the different cameras.
 4. The method of claim 2, in which performing the spatial-temporal transformations comprises: warping target images and source images captured by a same camera of the multi-camera rig at the different time-steps; and warping the context images and target images between different cameras and captured at the different time-steps according to a predicted ego-motion and known extrinsics of the different cameras.
 5. The method of claim 1, further comprising: predicting a transformation from a current frame to a subsequent frame captured by each camera of the multi-camera rig; transforming each predicted transformation to a coordinate frame of a canonical camera; constraining a translation vector and a rotation matrix of each camera according to the canonical camera; and generating the multi-camera PCC loss according to a translation loss and a rotation loss of the translation vector and the rotation matrix of each camera.
 6. The method of claim 1, in which training comprises enforcing pose consistency constraints to ensure cameras of the multi-camera rig follow a same rigid body motion to train scale-aware models without any ground-truth depth or ego-motion labels.
 7. The method of claim 1, in which training the depth estimation model and the ego-motion estimation model comprises leveraging cross-camera temporal contexts via spatio-temporal photometric constraints to increase an amount of overlap between cameras of the multi-camera rig using a predicted ego-motion of the ego vehicle.
 8. The method of claim 1, further comprising: predicting a 360° point cloud of a scene surrounding the ego vehicle according to the trained multi-camera depth estimation model and the ego-motion estimation model; and planning a trajectory of the ego vehicle according to the 360° point cloud of the scene surrounding the ego vehicle.
 9. A non-transitory computer-readable medium having program code recorded thereon for multi-camera monocular depth estimation using pose averaging, the program code being executed by a processor and comprising: program code to adjust a multi-camera photometric loss associated with a multi-camera rig of an ego vehicle according to a multi-camera pose consistency constraint (PCC) loss associated with the multi-camera rig of the ego vehicle to form a multi-camera PCC photometric loss; and program code to train a multi-camera depth estimation model and an ego-motion estimation model according to the multi-camera PCC photometric loss.
 10. The non-transitory computer-readable medium of claim 9, further comprises: program code to capture images of the scene surrounding the ego vehicle using the multi-camera rig of the ego vehicle, in which cameras of the multi-camera rig have a predetermined minimum overlap; program code to select target images and context images captured by the cameras of the multi-camera rig of the ego vehicle at a same time-step and at different time-steps; and program code to perform spatial-temporal transformations of selected target images and the context images to determine the multi-camera photometric loss.
 11. The non-transitory computer-readable medium of claim 10, in which the program code to perform spatial-temporal transformations comprises program code to warp the target images and the context images captured by different cameras of the multi-camera rig at the different time-steps according to a predicted ego-motion of the ego vehicle and known extrinsics of the different cameras.
 12. The non-transitory computer-readable medium of claim 10, in which the program code to perform spatial-temporal transformations comprises: program code to warp the target images and source images captured by a same camera of the multi-camera rig at the different time-steps; and program code to warp the context images and the target images between different cameras and captured at the different time-steps according to a predicted ego-motion and known extrinsics of the different cameras.
 13. The non-transitory computer-readable medium of claim 9, further comprising: program code to predict a transformation from a current frame to a subsequent frame captured by each camera of the multi-camera rig; program code to transform each predicted transformation to a coordinate frame of a canonical camera; program code to constrain a translation vector and a rotation matrix of each camera according to the canonical camera; and program code to generate the multi-camera PCC loss according to a translation loss and a rotation loss of the translation vector and the rotation matrix of each camera.
 14. The non-transitory computer-readable medium of claim 9, in which the program code to train comprises program code to enforce pose consistency constraints to ensure cameras of the multi-camera rig follow a same rigid body motion to train scale-aware models without any ground-truth depth or ego-motion labels.
 15. The non-transitory computer-readable medium of claim 9, in which the program code to train the depth estimation model and the ego-motion estimation model comprises program code to leverage cross-camera temporal contexts via spatio-temporal photometric constraints to increase an amount of overlap between cameras of the multi-camera rig using a predicted ego-motion of the ego vehicle.
 16. The non-transitory computer-readable medium of claim 9, further comprising: program code to predict a 360° point cloud of a scene surrounding the ego vehicle according to the trained multi-camera depth estimation model and the ego-motion estimation model program code to plan a trajectory of the ego vehicle according to the 360° point cloud of the scene surrounding the ego vehicle. 